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      STRING v9.1: protein-protein interaction networks, with increased coverage and integration

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          Abstract

          Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database ( http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

            Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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              The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

              GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2013
                January 2013
                29 November 2012
                29 November 2012
                : 41
                : D1 , Database issue
                : D808-D815
                Affiliations
                1Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Switzerland, 2Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark, 3Biotechnology Center, Technical University Dresden, Germany, 4Department of Computer Science, University of Milan, Italy, 5European Molecular Biology Laboratory, Heidelberg and 6Max-Delbrück-Centre for Molecular Medicine, Berlin, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +41 44 6353147; Fax: +41 44 6356864; Email: mering@ 123456imls.uzh.ch
                Correspondence may also be addressed to Peer Bork. Tel: +49 6221 387 8526; Fax: +49 6221 387 8517; Email: bork@ 123456embl.de
                Correspondence may also be addressed to Lars J. Jensen. Tel: +45 35 32 50 25; Fax: +45 35 32 50 01; Email: lars.juhl.jensen@ 123456cpr.ku.dk

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors.

                Article
                gks1094
                10.1093/nar/gks1094
                3531103
                23203871
                fc3e89f3-f4ed-43fc-8f43-b789c5b0fa1c
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.

                History
                : 15 September 2012
                : 15 October 2012
                : 18 October 2012
                Page count
                Pages: 8
                Categories
                Articles

                Genetics
                Genetics

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